text: #
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  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  import functools
  import pydoc
  import shutil
  import tempfile
  import unittest
  import datetime
  
  from pyspark import SparkContext, SQLContext
  from pyspark.sql import SparkSession, Column, Row
  from pyspark.sql.functions import udf, assert_true, lit
  from pyspark.sql.udf import UserDefinedFunction
  from pyspark.sql.types import (
      StringType,
      IntegerType,
      BooleanType,
      DoubleType,
      LongType,
      ArrayType,
      StructType,
      StructField,
      TimestampNTZType,
      DayTimeIntervalType,
  )
  from pyspark.sql.utils import AnalysisException
  from pyspark.testing.sqlutils import ReusedSQLTestCase, test_compiled, test_not_compiled_message
  from pyspark.testing.utils import QuietTest
  
  
  class UDFTests(ReusedSQLTestCase):
      def test_udf_with_callable(self):
          d = [Row(number=i, squared=i ** 2) for i in range(10)]
          rdd = self.sc.parallelize(d)
          data = self.spark.createDataFrame(rdd)
  
          class PlusFour:
              def __call__(self, col):
                  if col is not None:
                      return col + 4
  
          call = PlusFour()
          pudf = UserDefinedFunction(call, LongType())
          res = data.select(pudf(data["number"]).alias("plus_four"))
          self.assertEqual(res.agg({"plus_four": "sum"}).collect()[0][0], 85)
  
      def test_udf_with_partial_function(self):
          d = [Row(number=i, squared=i ** 2) for i in range(10)]
          rdd = self.sc.parallelize(d)
          data = self.spark.createDataFrame(rdd)
  
          def some_func(col, param):
              if col is not None:
                  return col + param
  
          pfunc = functools.partial(some_func, param=4)
          pudf = UserDefinedFunction(pfunc, LongType())
          res = data.select(pudf(data["number"]).alias("plus_four"))
          self.assertEqual(res.agg({"plus_four": "sum"}).collect()[0][0], 85)
  
      def test_udf(self):
          self.spark.catalog.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
          [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
          self.assertEqual(row[0], 5)
  
          # This is to check if a deprecated 'SQLContext.registerFunction' can call its alias.
          sqlContext = SQLContext.getOrCreate(self.spark.sparkContext)
          sqlContext.registerFunction("oneArg", lambda x: len(x), IntegerType())
          [row] = sqlContext.sql("SELECT oneArg('test')").collect()
          self.assertEqual(row[0], 4)
  
      def test_udf2(self):
          with self.tempView("test"):
              self.spark.catalog.registerFunction("strlen", lambda string: len(string), IntegerType())
              self.spark.createDataFrame(
                  self.sc.parallelize([Row(a="test")])
              ).createOrReplaceTempView("test")
              [res] = self.spark.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
              self.assertEqual(4, res[0])
  
      def test_udf3(self):
          two_args = self.spark.catalog.registerFunction(
              "twoArgs", UserDefinedFunction(lambda x, y: len(x) + y)
          )
          self.assertEqual(two_args.deterministic, True)
          [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
          self.assertEqual(row[0], "5")
  
      def test_udf_registration_return_type_none(self):
          two_args = self.spark.catalog.registerFunction(
              "twoArgs", UserDefinedFunction(lambda x, y: len(x) + y, "integer"), None
          )
          self.assertEqual(two_args.deterministic, True)
          [row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
          self.assertEqual(row[0], 5)
  
      def test_udf_registration_return_type_not_none(self):
          with QuietTest(self.sc):
              with self.assertRaisesRegex(TypeError, "Invalid return type"):
                  self.spark.catalog.registerFunction(
                      "f", UserDefinedFunction(lambda x, y: len(x) + y, StringType()), StringType()
                  )
  
      def test_nondeterministic_udf(self):
          # Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations
          import random
  
          udf_random_col = udf(lambda: int(100 * random.random()), IntegerType()).asNondeterministic()
          self.assertEqual(udf_random_col.deterministic, False)
          df = self.spark.createDataFrame([Row(1)]).select(udf_random_col().alias("RAND"))
          udf_add_ten = udf(lambda rand: rand + 10, IntegerType())
          [row] = df.withColumn("RAND_PLUS_TEN", udf_add_ten("RAND")).collect()
          self.assertEqual(row[0] + 10, row[1])
  
      def test_nondeterministic_udf2(self):
          import random
  
          random_udf = udf(lambda: random.randint(6, 6), IntegerType()).asNondeterministic()
          self.assertEqual(random_udf.deterministic, False)
          random_udf1 = self.spark.catalog.registerFunction("randInt", random_udf)
          self.assertEqual(random_udf1.deterministic, False)
          [row] = self.spark.sql("SELECT randInt()").collect()
          self.assertEqual(row[0], 6)
          [row] = self.spark.range(1).select(random_udf1()).collect()
          self.assertEqual(row[0], 6)
          [row] = self.spark.range(1).select(random_udf()).collect()
          self.assertEqual(row[0], 6)
          # render_doc() reproduces the help() exception without printing output
          pydoc.render_doc(udf(lambda: random.randint(6, 6), IntegerType()))
          pydoc.render_doc(random_udf)
          pydoc.render_doc(random_udf1)
          pydoc.render_doc(udf(lambda x: x).asNondeterministic)
  
      def test_nondeterministic_udf3(self):
          # regression test for SPARK-23233
          f = udf(lambda x: x)
          # Here we cache the JVM UDF instance.
          self.spark.range(1).select(f("id"))
          # This should reset the cache to set the deterministic status correctly.
          f = f.asNondeterministic()
          # Check the deterministic status of udf.
          df = self.spark.range(1).select(f("id"))
          deterministic = df._jdf.logicalPlan().projectList().head().deterministic()
          self.assertFalse(deterministic)
  
      def test_nondeterministic_udf_in_aggregate(self):
          from pyspark.sql.functions import sum
          import random
  
          udf_random_col = udf(lambda: int(100 * random.random()), "int").asNondeterministic()
          df = self.spark.range(10)
  
          with QuietTest(self.sc):
              with self.assertRaisesRegex(AnalysisException, "nondeterministic"):
                  df.groupby("id").agg(sum(udf_random_col())).collect()
              with self.assertRaisesRegex(AnalysisException, "nondeterministic"):
                  df.agg(sum(udf_random_col())).collect()
  
      def test_chained_udf(self):
          self.spark.catalog.registerFunction("double", lambda x: x + x, IntegerType())
          [row] = self.spark.sql("SELECT double(1)").collect()
          self.assertEqual(row[0], 2)
          [row] = self.spark.sql("SELECT double(double(1))").collect()
          self.assertEqual(row[0], 4)
          [row] = self.spark.sql("SELECT double(double(1) + 1)").collect()
          self.assertEqual(row[0], 6)
  
      def test_single_udf_with_repeated_argument(self):
          # regression test for SPARK-20685
          self.spark.catalog.registerFunction("add", lambda x, y: x + y, IntegerType())
          row = self.spark.sql("SELECT add(1, 1)").first()
          self.assertEqual(tuple(row), (2,))
  
      def test_multiple_udfs(self):
          self.spark.catalog.registerFunction("double", lambda x: x * 2, IntegerType())
          [row] = self.spark.sql("SELECT double(1), double(2)").collect()
          self.assertEqual(tuple(row), (2, 4))
          [row] = self.spark.sql("SELECT double(double(1)), double(double(2) + 2)").collect()
          self.assertEqual(tuple(row), (4, 12))
          self.spark.catalog.registerFunction("add", lambda x, y: x + y, IntegerType())
          [row] = self.spark.sql("SELECT double(add(1, 2)), add(double(2), 1)").collect()
          self.assertEqual(tuple(row), (6, 5))
  
      def test_udf_in_filter_on_top_of_outer_join(self):
          left = self.spark.createDataFrame([Row(a=1)])
          right = self.spark.createDataFrame([Row(a=1)])
          df = left.join(right, on="a", how="left_outer")
          df = df.withColumn("b", udf(lambda x: "x")(df.a))
          self.assertEqual(df.filter('b = "x"').collect(), [Row(a=1, b="x")])
  
      def test_udf_in_filter_on_top_of_join(self):
          # regression test for SPARK-18589
          left = self.spark.createDataFrame([Row(a=1)])
          right = self.spark.createDataFrame([Row(b=1)])
          f = udf(lambda a, b: a == b, BooleanType())
          df = left.crossJoin(right).filter(f("a", "b"))
          self.assertEqual(df.collect(), [Row(a=1, b=1)])
  
      def test_udf_in_join_condition(self):
          # regression test for SPARK-25314
          left = self.spark.createDataFrame([Row(a=1)])
          right = self.spark.createDataFrame([Row(b=1)])
          f = udf(lambda a, b: a == b, BooleanType())
          # The udf uses attributes from both sides of join, so it is pulled out as Filter +
          # Cross join.
          df = left.join(right, f("a", "b"))
          with self.sql_conf({"spark.sql.crossJoin.enabled": False}):
              with self.assertRaisesRegex(AnalysisException, "Detected implicit cartesian product"):
                  df.collect()
          with self.sql_conf({"spark.sql.crossJoin.enabled": True}):
              self.assertEqual(df.collect(), [Row(a=1, b=1)])
  
      def test_udf_in_left_outer_join_condition(self):
          # regression test for SPARK-26147
          from pyspark.sql.functions import col
  
          left = self.spark.createDataFrame([Row(a=1)])
          right = self.spark.createDataFrame([Row(b=1)])
          f = udf(lambda a: str(a), StringType())
          # The join condition can't be pushed down, as it refers to attributes from both sides.
          # The Python UDF only refer to attributes from one side, so it's evaluable.
          df = left.join(right, f("a") == col("b").cast("string"), how="left_outer")
          with self.sql_conf({"spark.sql.crossJoin.enabled": True}):
              self.assertEqual(df.collect(), [Row(a=1, b=1)])
  
      def test_udf_and_common_filter_in_join_condition(self):
          # regression test for SPARK-25314
          # test the complex scenario with both udf and common filter
          left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
          right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
          f = udf(lambda a, b: a == b, BooleanType())
          df = left.join(right, [f("a", "b"), left.a1 == right.b1])
          # do not need spark.sql.crossJoin.enabled=true for udf is not the only join condition.
          self.assertEqual(df.collect(), [Row(a=1, a1=1, a2=1, b=1, b1=1, b2=1)])
  
      def test_udf_not_supported_in_join_condition(self):
          # regression test for SPARK-25314
          # test python udf is not supported in join type except inner join.
          left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
          right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
          f = udf(lambda a, b: a == b, BooleanType())
  
          def runWithJoinType(join_type, type_string):
              with self.assertRaisesRegex(
                  AnalysisException,
                  """Python UDF in the ON clause of a %s JOIN.""" % type_string,
              ):
                  left.join(right, [f("a", "b"), left.a1 == right.b1], join_type).collect()
  
          runWithJoinType("full", "FULL OUTER")
          runWithJoinType("left", "LEFT OUTER")
          runWithJoinType("right", "RIGHT OUTER")
          runWithJoinType("leftanti", "LEFT ANTI")
          runWithJoinType("leftsemi", "LEFT SEMI")
  
      def test_udf_as_join_condition(self):
          left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
          right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
          f = udf(lambda a: a, IntegerType())
  
          df = left.join(right, [f("a") == f("b"), left.a1 == right.b1])
          self.assertEqual(df.collect(), [Row(a=1, a1=1, a2=1, b=1, b1=1, b2=1)])
  
      def test_udf_without_arguments(self):
          self.spark.catalog.registerFunction("foo", lambda: "bar")
          [row] = self.spark.sql("SELECT foo()").collect()
          self.assertEqual(row[0], "bar")
  
      def test_udf_with_array_type(self):
          with self.tempView("test"):
              d = [Row(l=list(range(3)), d={"key": list(range(5))})]
              rdd = self.sc.parallelize(d)
              self.spark.createDataFrame(rdd).createOrReplaceTempView("test")
              self.spark.catalog.registerFunction(
                  "copylist", lambda l: list(l), ArrayType(IntegerType())
              )
              self.spark.catalog.registerFunction("maplen", lambda d: len(d), IntegerType())
              [(l1, l2)] = self.spark.sql("select copylist(l), maplen(d) from test").collect()
              self.assertEqual(list(range(3)), l1)
              self.assertEqual(1, l2)
  
      def test_broadcast_in_udf(self):
          bar = {"a": "aa", "b": "bb", "c": "abc"}
          foo = self.sc.broadcast(bar)
          self.spark.catalog.registerFunction("MYUDF", lambda x: foo.value[x] if x else "")
          [res] = self.spark.sql("SELECT MYUDF('c')").collect()
          self.assertEqual("abc", res[0])
          [res] = self.spark.sql("SELECT MYUDF('')").collect()
          self.assertEqual("", res[0])
  
      def test_udf_with_filter_function(self):
          df = self.spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
          from pyspark.sql.functions import col
  
          my_filter = udf(lambda a: a < 2, BooleanType())
          sel = df.select(col("key"), col("value")).filter((my_filter(col("key"))) & (df.value < "2"))
          self.assertEqual(sel.collect(), [Row(key=1, value="1")])
  
      def test_udf_with_aggregate_function(self):
          df = self.spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
          from pyspark.sql.functions import col, sum
  
          my_filter = udf(lambda a: a == 1, BooleanType())
          sel = df.select(col("key")).distinct().filter(my_filter(col("key")))
          self.assertEqual(sel.collect(), [Row(key=1)])
  
          my_copy = udf(lambda x: x, IntegerType())
          my_add = udf(lambda a, b: int(a + b), IntegerType())
          my_strlen = udf(lambda x: len(x), IntegerType())
          sel = (
              df.groupBy(my_copy(col("key")).alias("k"))
              .agg(sum(my_strlen(col("value"))).alias("s"))
              .select(my_add(col("k"), col("s")).alias("t"))
          )
          self.assertEqual(sel.collect(), [Row(t=4), Row(t=3)])
  
      def test_udf_in_generate(self):
          from pyspark.sql.functions import explode
  
          df = self.spark.range(5)
          f = udf(lambda x: list(range(x)), ArrayType(LongType()))
          row = df.select(explode(f(*df))).groupBy().sum().first()
          self.assertEqual(row[0], 10)
  
          df = self.spark.range(3)
          res = df.select("id", explode(f(df.id))).collect()
          self.assertEqual(res[0][0], 1)
          self.assertEqual(res[0][1], 0)
          self.assertEqual(res[1][0], 2)
          self.assertEqual(res[1][1], 0)
          self.assertEqual(res[2][0], 2)
          self.assertEqual(res[2][1], 1)
  
          range_udf = udf(lambda value: list(range(value - 1, value + 1)), ArrayType(IntegerType()))
          res = df.select("id", explode(range_udf(df.id))).collect()
          self.assertEqual(res[0][0], 0)
          self.assertEqual(res[0][1], -1)
          self.assertEqual(res[1][0], 0)
          self.assertEqual(res[1][1], 0)
          self.assertEqual(res[2][0], 1)
          self.assertEqual(res[2][1], 0)
          self.assertEqual(res[3][0], 1)
          self.assertEqual(res[3][1], 1)
  
      def test_udf_with_order_by_and_limit(self):
          my_copy = udf(lambda x: x, IntegerType())
          df = self.spark.range(10).orderBy("id")
          res = df.select(df.id, my_copy(df.id).alias("copy")).limit(1)
          self.assertEqual(res.collect(), [Row(id=0, copy=0)])
  
      def test_udf_registration_returns_udf(self):
          df = self.spark.range(10)
          add_three = self.spark.udf.register("add_three", lambda x: x + 3, IntegerType())
  
          self.assertListEqual(
              df.selectExpr("add_three(id) AS plus_three").collect(),
              df.select(add_three("id").alias("plus_three")).collect(),
          )
  
          # This is to check if a 'SQLContext.udf' can call its alias.
          sqlContext = SQLContext.getOrCreate(self.spark.sparkContext)
          add_four = sqlContext.udf.register("add_four", lambda x: x + 4, IntegerType())
  
          self.assertListEqual(
              df.selectExpr("add_four(id) AS plus_four").collect(),
              df.select(add_four("id").alias("plus_four")).collect(),
          )
  
      @unittest.skipIf(not test_compiled, test_not_compiled_message)  # type: ignore
      def test_register_java_function(self):
          self.spark.udf.registerJavaFunction(
              "javaStringLength", "test.org.apache.spark.sql.JavaStringLength", IntegerType()
          )
          [value] = self.spark.sql("SELECT javaStringLength('test')").first()
          self.assertEqual(value, 4)
  
          self.spark.udf.registerJavaFunction(
              "javaStringLength2", "test.org.apache.spark.sql.JavaStringLength"
          )
          [value] = self.spark.sql("SELECT javaStringLength2('test')").first()
          self.assertEqual(value, 4)
  
          self.spark.udf.registerJavaFunction(
              "javaStringLength3", "test.org.apache.spark.sql.JavaStringLength", "integer"
          )
          [value] = self.spark.sql("SELECT javaStringLength3('test')").first()
          self.assertEqual(value, 4)
  
      @unittest.skipIf(not test_compiled, test_not_compiled_message)  # type: ignore
      def test_register_java_udaf(self):
          self.spark.udf.registerJavaUDAF("javaUDAF", "test.org.apache.spark.sql.MyDoubleAvg")
          df = self.spark.createDataFrame([(1, "a"), (2, "b"), (3, "a")], ["id", "name"])
          df.createOrReplaceTempView("df")
          row = self.spark.sql(
              "SELECT name, javaUDAF(id) as avg from df group by name order by name desc"
          ).first()
          self.assertEqual(row.asDict(), Row(name="b", avg=102.0).asDict())
  
      def test_non_existed_udf(self):
          spark = self.spark
          self.assertRaisesRegex(
              AnalysisException,
              "Can not load class non_existed_udf",
              lambda: spark.udf.registerJavaFunction("udf1", "non_existed_udf"),
          )
  
          # This is to check if a deprecated 'SQLContext.registerJavaFunction' can call its alias.
          sqlContext = SQLContext.getOrCreate(self.spark.sparkContext)
          self.assertRaisesRegex(
              AnalysisException,
              "Can not load class non_existed_udf",
              lambda: sqlContext.registerJavaFunction("udf1", "non_existed_udf"),
          )
  
      def test_non_existed_udaf(self):
          spark = self.spark
          self.assertRaisesRegex(
              AnalysisException,
              "Can not load class non_existed_udaf",
              lambda: spark.udf.registerJavaUDAF("udaf1", "non_existed_udaf"),
          )
  
      def test_udf_with_input_file_name(self):
          from pyspark.sql.functions import input_file_name
  
          sourceFile = udf(lambda path: path, StringType())
          filePath = "python/test_support/sql/people1.json"
          row = self.spark.read.json(filePath).select(sourceFile(input_file_name())).first()
          self.assertTrue(row[0].find("people1.json") != -1)
  
      def test_udf_with_input_file_name_for_hadooprdd(self):
          from pyspark.sql.functions import input_file_name
  
          def filename(path):
              return path
  
          sameText = udf(filename, StringType())
  
          rdd = self.sc.textFile("python/test_support/sql/people.json")
          df = self.spark.read.json(rdd).select(input_file_name().alias("file"))
          row = df.select(sameText(df["file"])).first()
          self.assertTrue(row[0].find("people.json") != -1)
  
          rdd2 = self.sc.newAPIHadoopFile(
              "python/test_support/sql/people.json",
              "org.apache.hadoop.mapreduce.lib.input.TextInputFormat",
              "org.apache.hadoop.io.LongWritable",
              "org.apache.hadoop.io.Text",
          )
  
          df2 = self.spark.read.json(rdd2).select(input_file_name().alias("file"))
          row2 = df2.select(sameText(df2["file"])).first()
          self.assertTrue(row2[0].find("people.json") != -1)
  
      def test_udf_defers_judf_initialization(self):
          # This is separate of  UDFInitializationTests
          # to avoid context initialization
          # when udf is called
          f = UserDefinedFunction(lambda x: x, StringType())
  
          self.assertIsNone(
              f._judf_placeholder, "judf should not be initialized before the first call."
          )
  
          self.assertIsInstance(f("foo"), Column, "UDF call should return a Column.")
  
          self.assertIsNotNone(
              f._judf_placeholder, "judf should be initialized after UDF has been called."
          )
  
      def test_udf_with_string_return_type(self):
          add_one = UserDefinedFunction(lambda x: x + 1, "integer")
          make_pair = UserDefinedFunction(lambda x: (-x, x), "struct<x:integer,y:integer>")
          make_array = UserDefinedFunction(
              lambda x: [float(x) for x in range(x, x + 3)], "array<double>"
          )
  
          expected = (2, Row(x=-1, y=1), [1.0, 2.0, 3.0])
          actual = (
              self.spark.range(1, 2)
              .toDF("x")
              .select(add_one("x"), make_pair("x"), make_array("x"))
              .first()
          )
  
          self.assertTupleEqual(expected, actual)
  
      def test_udf_should_not_accept_noncallable_object(self):
          non_callable = None
          self.assertRaises(TypeError, UserDefinedFunction, non_callable, StringType())
  
      def test_udf_with_decorator(self):
          from pyspark.sql.functions import lit
  
          @udf(IntegerType())
          def add_one(x):
              if x is not None:
                  return x + 1
  
          @udf(returnType=DoubleType())
          def add_two(x):
              if x is not None:
                  return float(x + 2)
  
          @udf
          def to_upper(x):
              if x is not None:
                  return x.upper()
  
          @udf()
          def to_lower(x):
              if x is not None:
                  return x.lower()
  
          @udf
          def substr(x, start, end):
              if x is not None:
                  return x[start:end]
  
          @udf("long")
          def trunc(x):
              return int(x)
  
          @udf(returnType="double")
          def as_double(x):
              return float(x)
  
          df = self.spark.createDataFrame(
              [(1, "Foo", "foobar", 3.0)], ("one", "Foo", "foobar", "float")
          ).select(
              add_one("one"),
              add_two("one"),
              to_upper("Foo"),
              to_lower("Foo"),
              substr("foobar", lit(0), lit(3)),
              trunc("float"),
              as_double("one"),
          )
  
          self.assertListEqual(
              [tpe for _, tpe in df.dtypes],
              ["int", "double", "string", "string", "string", "bigint", "double"],
          )
  
          self.assertListEqual(list(df.first()), [2, 3.0, "FOO", "foo", "foo", 3, 1.0])
  
      def test_udf_wrapper(self):
          def f(x):
              """Identity"""
              return x
  
          return_type = IntegerType()
          f_ = udf(f, return_type)
  
          self.assertTrue(f.__doc__ in f_.__doc__)
          self.assertEqual(f, f_.func)
          self.assertEqual(return_type, f_.returnType)
  
          class F:
              """Identity"""
  
              def __call__(self, x):
                  return x
  
          f = F()
          return_type = IntegerType()
          f_ = udf(f, return_type)
  
          self.assertTrue(f.__doc__ in f_.__doc__)
          self.assertEqual(f, f_.func)
          self.assertEqual(return_type, f_.returnType)
  
          f = functools.partial(f, x=1)
          return_type = IntegerType()
          f_ = udf(f, return_type)
  
          self.assertTrue(f.__doc__ in f_.__doc__)
          self.assertEqual(f, f_.func)
          self.assertEqual(return_type, f_.returnType)
  
      def test_udf_timestamp_ntz(self):
          # SPARK-36626: Test TimestampNTZ in Python UDF
          @udf(TimestampNTZType())
          def noop(x):
              assert x == datetime.datetime(1970, 1, 1, 0, 0)
              return x
  
          with self.sql_conf({"spark.sql.session.timeZone": "Pacific/Honolulu"}):
              df = self.spark.createDataFrame(
                  [(datetime.datetime(1970, 1, 1, 0, 0),)], schema="dt timestamp_ntz"
              ).select(noop("dt").alias("dt"))
  
              df.selectExpr("assert_true('1970-01-01 00:00:00' == CAST(dt AS STRING))").collect()
              self.assertEqual(df.schema[0].dataType.typeName(), "timestamp_ntz")
              self.assertEqual(df.first()[0], datetime.datetime(1970, 1, 1, 0, 0))
  
      def test_udf_daytime_interval(self):
          # SPARK-37277: Support DayTimeIntervalType in Python UDF
          @udf(DayTimeIntervalType(DayTimeIntervalType.DAY, DayTimeIntervalType.SECOND))
          def noop(x):
              assert x == datetime.timedelta(microseconds=123)
              return x
  
          df = self.spark.createDataFrame(
              [(datetime.timedelta(microseconds=123),)], schema="td interval day to second"
          ).select(noop("td").alias("td"))
  
          df.select(
              assert_true(lit("INTERVAL '0 00:00:00.000123' DAY TO SECOND") == df.td.cast("string"))
          ).collect()
          self.assertEqual(df.schema[0].dataType.simpleString(), "interval day to second")
          self.assertEqual(df.first()[0], datetime.timedelta(microseconds=123))
  
      def test_nonparam_udf_with_aggregate(self):
          import pyspark.sql.functions as f
  
          df = self.spark.createDataFrame([(1, 2), (1, 2)])
          f_udf = f.udf(lambda: "const_str")
          rows = df.distinct().withColumn("a", f_udf()).collect()
          self.assertEqual(rows, [Row(_1=1, _2=2, a="const_str")])
  
      # SPARK-24721
      @unittest.skipIf(not test_compiled, test_not_compiled_message)  # type: ignore
      def test_datasource_with_udf(self):
          from pyspark.sql.functions import lit, col
  
          path = tempfile.mkdtemp()
          shutil.rmtree(path)
  
          try:
              self.spark.range(1).write.mode("overwrite").format("csv").save(path)
              filesource_df = self.spark.read.option("inferSchema", True).csv(path).toDF("i")
              datasource_df = (
                  self.spark.read.format("org.apache.spark.sql.sources.SimpleScanSource")
                  .option("from", 0)
                  .option("to", 1)
                  .load()
                  .toDF("i")
              )
              datasource_v2_df = (
                  self.spark.read.format("org.apache.spark.sql.connector.SimpleDataSourceV2")
                  .load()
                  .toDF("i", "j")
              )
  
              c1 = udf(lambda x: x + 1, "int")(lit(1))
              c2 = udf(lambda x: x + 1, "int")(col("i"))
  
              f1 = udf(lambda x: False, "boolean")(lit(1))
              f2 = udf(lambda x: False, "boolean")(col("i"))
  
              for df in [filesource_df, datasource_df, datasource_v2_df]:
                  result = df.withColumn("c", c1)
                  expected = df.withColumn("c", lit(2))
                  self.assertEqual(expected.collect(), result.collect())
  
              for df in [filesource_df, datasource_df, datasource_v2_df]:
                  result = df.withColumn("c", c2)
                  expected = df.withColumn("c", col("i") + 1)
                  self.assertEqual(expected.collect(), result.collect())
  
              for df in [filesource_df, datasource_df, datasource_v2_df]:
                  for f in [f1, f2]:
                      result = df.filter(f)
                      self.assertEqual(0, result.count())
          finally:
              shutil.rmtree(path)
  
      # SPARK-25591
      def test_same_accumulator_in_udfs(self):
          data_schema = StructType(
              [StructField("a", IntegerType(), True), StructField("b", IntegerType(), True)]
          )
          data = self.spark.createDataFrame([[1, 2]], schema=data_schema)
  
          test_accum = self.sc.accumulator(0)
  
          def first_udf(x):
              test_accum.add(1)
              return x
  
          def second_udf(x):
              test_accum.add(100)
              return x
  
          func_udf = udf(first_udf, IntegerType())
          func_udf2 = udf(second_udf, IntegerType())
          data = data.withColumn("out1", func_udf(data["a"]))
          data = data.withColumn("out2", func_udf2(data["b"]))
          data.collect()
          self.assertEqual(test_accum.value, 101)
  
      # SPARK-26293
      def test_udf_in_subquery(self):
          f = udf(lambda x: x, "long")
          with self.tempView("v"):
              self.spark.range(1).filter(f("id") >= 0).createTempView("v")
              sql = self.spark.sql
              result = sql("select i from values(0L) as data(i) where i in (select id from v)")
              self.assertEqual(result.collect(), [Row(i=0)])
  
      def test_udf_globals_not_overwritten(self):
          @udf("string")
          def f():
              assert "itertools" not in str(map)
  
          self.spark.range(1).select(f()).collect()
  
      def test_worker_original_stdin_closed(self):
          # Test if it closes the original standard input of worker inherited from the daemon,
          # and replaces it with '/dev/null'.  See SPARK-26175.
          def task(iterator):
              import sys
  
              res = sys.stdin.read()
              # Because the standard input is '/dev/null', it reaches to EOF.
              assert res == "", "Expect read EOF from stdin."
              return iterator
  
          self.sc.parallelize(range(1), 1).mapPartitions(task).count()
  
      def test_udf_with_256_args(self):
          N = 256
          data = [["data-%d" % i for i in range(N)]] * 5
          df = self.spark.createDataFrame(data)
  
          def f(*a):
              return "success"
  
          fUdf = udf(f, StringType())
  
          r = df.select(fUdf(*df.columns))
          self.assertEqual(r.first()[0], "success")
  
      def test_udf_cache(self):
          def func(x):
              return x
  
          df = self.spark.range(1)
          df.select(udf(func)("id")).cache()
  
          self.assertEqual(
              df.select(udf(func)("id"))
              ._jdf.queryExecution()
              .withCachedData()
              .getClass()
              .getSimpleName(),
              "InMemoryRelation",
          )
  
      # SPARK-34545
      def test_udf_input_serialization_valuecompare_disabled(self):
          def f(e):
              return e[0]
  
          df = self.spark.createDataFrame([((1.0, 1.0), (1, 1))], ["c1", "c2"])
          result = df.select(
              "*", udf(f, DoubleType())("c1").alias("c3"), udf(f, IntegerType())("c2").alias("c4")
          )
          self.assertEqual(
              result.collect(), [Row(c1=Row(_1=1.0, _2=1.0), c2=Row(_1=1, _2=1), c3=1.0, c4=1)]
          )
  
      # SPARK-33277
      def test_udf_with_column_vector(self):
          path = tempfile.mkdtemp()
          shutil.rmtree(path)
  
          try:
              self.spark.range(0, 100000, 1, 1).write.parquet(path)
  
              def f(x):
                  return 0
  
              fUdf = udf(f, LongType())
  
              for offheap in ["true", "false"]:
                  with self.sql_conf({"spark.sql.columnVector.offheap.enabled": offheap}):
                      self.assertEquals(
                          self.spark.read.parquet(path).select(fUdf("id")).head(), Row(0)
                      )
          finally:
              shutil.rmtree(path)
  
  
  class UDFInitializationTests(unittest.TestCase):
      def tearDown(self):
          if SparkSession._instantiatedSession is not None:
              SparkSession._instantiatedSession.stop()
  
          if SparkContext._active_spark_context is not None:
              SparkContext._active_spark_context.stop()
  
      def test_udf_init_should_not_initialize_context(self):
          UserDefinedFunction(lambda x: x, StringType())
  
          self.assertIsNone(
              SparkContext._active_spark_context,
              "SparkContext shouldn't be initialized when UserDefinedFunction is created.",
          )
          self.assertIsNone(
              SparkSession._instantiatedSession,
              "SparkSession shouldn't be initialized when UserDefinedFunction is created.",
          )
  
  
  if __name__ == "__main__":
      from pyspark.sql.tests.test_udf import *  # noqa: F401
  
      try:
          import xmlrunner  # type: ignore
  
          testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  